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library(OpasnetUtils)
library(plotly)

## Loading required package: ggplot2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

#Paketti datan editoimiseen ja visualisointiin
 library(tidyverse)

## ── Attaching packages ──────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ tibble  3.0.1     ✓ dplyr   1.0.0
## ✓ tidyr   1.1.0     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ✓ purrr   0.3.4

## ── Conflicts ─────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::combine() masks OpasnetUtils::combine()
## x dplyr::filter()  masks plotly::filter(), stats::filter()
## x dplyr::lag()     masks stats::lag()

if(FALSE){ 
#Paketti datan lukemiseen
library(jsonlite)
library(rjson)

#Luetaan aineisto sisään
aineisto <- fromJSON("https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.json?row=dateweek2020010120201231-443686&column=hcdmunicipality2020-445222")

dat <- read.csv("https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.csv?row=dateweek2020010120201231-443686&row=hcdmunicipality2020-445222&column=measure-141082", sep=";")

dat <- read.csv("https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.csv?row=dateweek2020010120201231-443686&row=hcdmunicipality2020-445131&column=measure-141082", sep=";")

meta <- readLines("https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.dimensions.json")
} # ENDIF

#Paketti datan lukemiseen
library(jsonlite)

## 
## Attaching package: 'jsonlite'

## The following object is masked from 'package:purrr':
## 
##     flatten

library(rjson)

## 
## Attaching package: 'rjson'

## The following objects are masked from 'package:jsonlite':
## 
##     fromJSON, toJSON

dat <- jsonlite::fromJSON("https://plus.yle.fi/lambda_sheets/korona/2020-04-municipalities-infections-cumulative/data.json")
dat <- as.data.frame(dat$data)
dat$date <- as.Date(dat$date, format="%d.%m.%Y")
dat$cumulative[dat$cumulative==".."] <- 0
dat$cumulative <- as.numeric(as.character(dat$cumulative))
dat$new <- as.numeric(as.character(dat$new))
dat$perhundredthousand <- as.numeric(as.character(dat$perhundredthousand))

geojson <- rjson::fromJSON(file="https://github.com/teelmo/geodata/raw/master/geojson/Kuntarajat%202017.geojson")

g <- list(
  fitbounds = "geojson",
  projection="albers",
  visible = FALSE
)

### Kartta kaikista raportoiduista tapauksista
if(FALSE) {
tmp <- dat[dat$date==max(dat$date),]
fig <- plot_ly() %>%
   add_trace(
    type="choropleth",
    geojson=geojson,
    locations=tmp$area,
    z=tmp$cumulative,
    zmin=0,
    zmax=50,
    colorscale="Viridis",
    featureidkey="properties.Name"
  ) %>%
  layout(
    geo = g
  )
fig

pushIndicatorGraph(fig, 395)
}

### Kartta tartuttavista (eli viimeisten 20 päivän tapauksista)

tmp <- dat[dat$date>=max(dat$date)-20,]
tmp <- aggregate(tmp$new, by=tmp[c("area")], FUN=function(x) sum(x, na.rm=TRUE))
fig <- plot_ly() %>%
   add_trace(
    type="choropleth",
    geojson=geojson,
    locations=tmp$area,
    z=tmp$x,
    zmin=0,
    zmax=30,
    colorscale="Viridis",
    featureidkey="properties.Name"
  ) %>%
  layout(
    geo = g
  )
fig

## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

tmp <- tmp[order(-tmp$x),]

cat("There are", sum(tmp$x), "infective corona cases in Finland right now.\n")

## There are 252 infective corona cases in Finland right now.

tmp

############################################

if(FALSE){
url <- 'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'
counties <- rjson::fromJSON(file=url)
url2<- "https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv"
df <- read.csv(url2, colClasses=c(fips="character"))
g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)
fig <- plot_ly()
fig <- fig %>% add_trace(
    type="choropleth",
    geojson=counties,
    locations=df$fips,
    z=df$unemp,
    colorscale="Viridis",
    zmin=0,
    zmax=12,
    marker=list(line=list(
      width=0)
    )
  )
fig <- fig %>% colorbar(title = "Unemployment Rate (%)")
fig <- fig %>% layout(
    title = "2016 US Unemployment by County"
)

fig <- fig %>% layout(
    geo = g
  )

fig

#################################################

shp <- substr(meta[grep("SHP",meta)-1],8,13)
day <- substr(meta[grep(':"week"',meta)-2],8,13)

l1 <- "https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.csv?row=dateweek2020010120201231-"
l1j <- "https://sampo.thl.fi/pivot/prod/fi/epirapo/covid19case/fact_epirapo_covid19case.json?row=dateweek2020010120201231-"
l2 <- "&row=hcdmunicipality2020-"
l3 <- "&column=measure-141082"

out <- data.frame()
for(i in shp) {
   for(j in day) {
      out <- rbind(out, read.csv(paste0(l1, j, l2, i, l3),sep=";"))
   }
}
out <- data.frame()
for(i in shp) {
   for(j in "443686") {
      out <- rbind(out, read.csv(paste0(l1, j, l2, i, l3),sep=";"))
   }
}
dat <- out
dat$val <- as.numeric(as.character(dat$val))
dat <- na.omit(dat)
dat$Time <- as.numeric(substr(dat$Aika,19,20))
dat$Day <- as.Date(dat$Aika)

library(ggplot2) 
library(plotly)

plot_ly(dat[dat$Mittari=="Tapausten lukumäärä",], x=~Time, y=~val, color=~Alue, type="scatter", mode="markers&lines") %>% 
   layout(title="Tapausten lukumäärä")

ggplot(dat[dat$Mittari=="Tapausten lukumäärä" & !grepl("SHP",dat$Alue),], aes(x=Time, y=val,colour=Alue))+geom_line()+facet_wrap(~Mittari)

ggplot(dat[dat$Mittari=="Tapausten lukumäärä",], aes(x=Day, y=val,colour=Alue))+geom_line()+facet_wrap(~Mittari)


tmp <- data.frame(value=unlist(aineisto$dataset$value))
tmp$value <- as.numeric(tmp$value)
tmp <- rownames_to_column(tmp)

tmp <- merge(data.frame(rowname=1:prod(aineisto$dataset$dimension$size)),tmp,all=TRUE)

tmp <- matrix(tmp$value,nrow=aineisto$dataset$dimension$size[2], dimnames=list(
unlist(aineisto$dataset$dimension$hcdmunicipality2020$category$label),
unlist(aineisto$dataset$dimension$dateweek2020010120201231$category$label)
))

#puretaan kategoriat paloiksi
 tmp <- aineisto$dataset$dimension$dateweek2020010120201231$category
 label <- data.frame(
   label=unlist(tmp$label),
   index=unlist(tmp$index),
   labelname=unlist(names(tmp$label)),
   indexname=unlist(names(tmp$label))
  )
  
 #Nimetään palaset
 names(label)<-"label"
 names(index)<-"index"
  
 #Laitetana vielä rivinumerot sarakkeiksi, jotta nämä saadaan yhteen.
 label<-rownames_to_column(label)
 index<-rownames_to_column(index)
  
 #Yhdistetään rivinimeä käyttäen
 kategoriat <- index %>% left_join(label,by="rowname")
 
 #otetaan data
 data <- as.data.frame(unlist(aineisto$dataset$value))
 
 #Nimetään
 names(data)<-"Tapauksien lkm"
 data<-rownames_to_column(data)
 data$rowname<-as.numeric(data$rowname)
 
#Yhdistetään muuhun aineistoon
 dataset <- kategoriat %>% left_join(data,by=c("index"="rowname"))
 
}

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